Topic: Accounting Data in Productivity Measurement and Strategic Decision Making

Name: ID

MD. Fakhrul Alam 1220185030

Rafid Habib 1220318030

MD. Azhar Uddin 1220321030

Accounting data in productivity measurement and strategic decision making:

As business strive to improve their profitability, it becomes vital to develop cost and profitability data that will support the firm’s desire to promote their most profitable products and identify their most profitable market segments. Further, information needs to be collected and classified to ensure the firm’s continuing commitment to enhanced productivity and adherence to budgeted performance. Research is focusing on the implications for a firm’s cost accounting system of these demands for improved productivity and profitability measurements.

Productivity improvement has become a key objective for industry. Productivity measurement, however has gone largely unnoticed by accounting professionals, particularly those teaching and doing research in accounting department and business school. Accounting textbooks virtually ignore issues of productivity measurement, and accounting journals contain few articles on the subject. Most articles on productivity measurement are written by economists- usually interested in productivity measurement at the national economy level-or by industrial engineers and production professionals.

The implicit, and occasionally explicit, rationalization for the accounting profession’s lack of interest in productivity measurement apparently arises from the belief that variances computed by the firm’s standard cost system, particularly usage variances computed by the firm’s standard cost system, particularly usage variances, are sufficient to measure the efficiency of the enterprise. Presumably, a desire for increased productivity could be signaled by across-the-board tightening of standards by the desired percentage. If a firm were inefficient or failed to meet its productivity improvement target, then the accounting system would report many unfavorable usage variances.

We do not believe that even well-designed and well-operated standard cost system provide adequate information for productivity measurement and improvement programs. Primarily this is an empirical statement. We observe numerous companies, with extensive standard cost accounting systems, establishing productivity measurement systems independent of the accounting system and even establishing separate departments to develop productivity statistics.

These companies apparently find a need to develop productivity measures to supplement the information being reported by their internal accounting systems.

The relationship between productivity measurement and the usage variances produced by standard cost systems has not been explored, nor is the relationship obvious. Further, because accounting researchers have not investigated productivity measurement in any depth, much of the considerable experience accounting scholars have gained during this century on the operation and analysis of standard cost system has not been applied to productivity measurements techniques.

In this paper, we attend to meet both goals. We articulate a role for productivity measurement that is not easily met by a collection of usage variances. We illustrate the relationship between productivity measurement and the output from a standard cost system. And we identify some shortcomings can be overcome by incorporating information from the standard cost system into the productivity measurement system. Thus this paper will bridge the current gap between the usage variance produce by accountants and the productivity measures produce by economists, industrial engineers, and operation managers. In the process, we develop improved performance measures by synthesizing the best aspect from each school of thought.

Productivity is a measured as a ratio of outputs to inputs:

Productivity=Quantity of outputs produced / Quantity of inputs consumed

By itself, a productivity measure has no meaning. It only gains meaning when compared to productivity measures for prior periods or to measures from comparable facilities producing similar outputs

Productivity measures attempt to highlight improvements in the physical use of resources, that is, to motivate and evaluate attempts to produce more outputs with fewer inputs while maintaining quality. By focusing on physical measures, outcomes are not influenced by changes in relative costs and prices. In the short run, profits can increase if output prices are raised faster than input costs are rising . In the long run, however, competitive market forces will prevent a farm from passing farm-specific, or even country –specific, cost increase on to customers. Sustainable competitive advantage arises only by having higher in productivity than competitors or by offering specialized products and services that competitors cannot match.

Productivity measures will permit managers to separate profit changes due to productivity factors and sells activity from those due to changes in output prices relative to input costs. Further, by linking productivity measures year to year, we obtain a dynamic , multi period evaluation of the organization’s performance. The annual changes in productivity and the organizations long-term productivity trend provide a convenient summary of operating performance.

In principle, managers wishing to show annual improvement in operation could compare usage variance from year to year. But this would not be meaningful if standards change from one year to the next, or if significant annual fluctuation in output occur. Usage variances are typically not computed as ratios and hence are not normalized for actual level of output. Basically, variance analysis provides only static, one –period retrospective analysis of performance. Although we will show, in a subsequent section of this paper, how to make variance analysis more dynamic, in practice we have not observed organization adopting a multi period perspective without using a summary measure of productivity. That is, when operating manager describes their improved performance, they tell to say, “Our productivity has been increasing by about 6% annually.” We do not hear them saying, “Our usage variances improve from $7million unfavorable to $1.5 million favorable.” note that the later statement, while telling us that operating performance has improved, doesn’t help us understand the significant of the cost improvement-whether it is a 3% or a 15% improvement.

Productivity measurement systems analyze performance based on actual outputs and input in different time periods. The productivity measurement system can be viewed as a variance of the actual costs incurred in successive period. In contrast, traditional management accounting system analyze performance within a given period by comparing actual quantities and prices to predetermined standards for quantities and prices.

When using an aggregate measure such as productivity, executive do not have to specify in detail how an annual productivity improvement should be achieved. Discretion is left to operating managers as to whether, for this year, the productivity gain will come from improved material utilization; more efficient, less nonproductive labor use; new capital investment; process improvement efforts; or overhead reductions.

Variance analysis is also a poor substitute for productivity measurement if standards are unrealistic or obsolete. In this unfortunately not uncommon situation, variances become difficult to interpret within a period, much less period to period. Even with a reasonably well functioning standard cost system, the method of assigning overhead to products can lead to mysterious variances. These variances arise from using cost centers and burden rates that are too aggregate and that do not reflect the actual consumption of the overhead resources by products passing through the cost centers.

Thus, relying on usage variances alone for motivating and evaluating productivity improvements can be ineffective if the standard cost system does not accurately reflect the current operating environment. In contrast, productivity measurement compares the ratio of actual outputs to actual inputs in each period and hence does not depend on having a well-functioning standard cost system. But productivity measurement is not without its own significant problems. Issues of aggregating multiple outputs and multiple inputs and how to measure and control for the use of capacity or fixed resources are among the set of issues we address in the remainder of this paper.

The accountant’s standard cost analysis implicitly assumes a separable and linear technology. The separable technology assumption requires that the quantities of each input (each material, labor, and overhead category) depend only on the quantity of output produced and not on the quantity used of any other input. That is, no trade-off exists among different classes of materials, labor, capital, or other overhead resources. A standard for consumption of certain material is defined without references to the amount of labor or any other material consumed to produce the output.

Linear (or constant marginal productivity) production technology assume that each of the variable inputs is consumed proportionally to increases in production volume. Therefore, to determine the standard amount of materials consumption for a given volume of output, we multiply the standard unit consumption of materials by the quantity of output produced. Since most productivity measurement system also implicitly assume a linear and separable production function. We maintain this assumption throughout our analysis in this paper too.

We illustrate the for computing productivity and price recovery variances by working through a numerical example. The example, although simple, has enough features to capture all the aspects that would be relevant even in realistic, more complex setting.

Table 1 describe the summary statistics for a company producing two products with inputs of a single raw material, a single grade of labor, and various types of overhead. In addition to distinguish between two types of fixed overhead: committed and discretionary. Committed overhead includes the cost of resources, such as the factory building (depreciation, insurance, taxes) and the plant manage, that cannot be reduced without shutting down the production facility entirely. Committed overhead costs tend to be associated with indivisible resources that consist of only one unit (e.g., one building, one plant manager, one machine). Therefore, we should not normally think of obtaining productivity gains from committed overhead resources. Fluctuations in committed overhead costs arise from price changes for these resources and not from using more or fewer of them.

Discretionary overhead, in contrast, consists of most plant overhead expenses that are incurred at the discretion of management and can be influenced by managerial decisions. Therefore, a primary focus of productivity programs may be to accomplish the same amount of work with fewer discretionary overhead resources. Discretionary overhead resources may be fixed with respect to short-term fluctuations in the volume of production, but they will vary with other transactions or managerial decisions. For example, the costs of the set-up department and the production control department will vary with the number of set-ups and the degree of diversity in the product line (Cooper and Kaplan [1987]). Therefore, the quality of discretionary overhead can be lowered by reducing complexity or product diversity in the factory, or by process- improving innovations, such as reducing set-up times and implementing just-in-time production systems. Such complexity reductions and process improvements will lower the demand for personnel in departments responsible for set-ups, inventory control, scheduling, and quality assurance, thereby permitting productivity gains with respect to discretionary overhead resources.

In general, any overhead department or cost center with more than one employee or one machine should be considered either a variable or a discretionary overhead account. For simplicity in our numeric example, we assume that all discretionary fixed” overhead is driven by one type of transaction, such as number of set-ups.

The variance analyses use the decomposition approach introduced by Shank and Churchill (1977; hereafter, SC).This approach provides a convenient method for analyzing the difference between actual and budgeted profits by decomposing the profit variance into components related to changes in sales activity and to changes in prices, costs, and efficiency. We extend the SC

analysis by combining individual price and cost variances into an overall price recovery variance, and by introducing a productivity variance to represent the aggregation of usage variances. The actual computations for our proposed three-variance system are shown in table 2.

First we compute a sales activity variance, shown in panel A of Table 2, to represent the change in profits caused by variation in the actual mix and volume of products produced and sold. The difference between actual and budgeted output quantities is evaluated at the standard margin for each output product. In this example, an unfavorable sales activity variance of -$2000 occurs because the increased sales of the low-margin product A (10,000 extra units at $2/unite) is offset by the lower sales of the high-margin Product B (5,000 fewer units at $4.40/unite)

In some productivity measurement system such as van Loggerenberg and Cucchiaro (1981-82), the sales activity variance is called a capacity usage variance and is considered a component of the overall productivity measure. We believe such treatment is inappropriate for two reasons. Firsts, it is semantically misleading to refer to the higher profits earned by more favorable volume or mix of sales as due to capacity utilization. Capacity usage conjures up the spreading of “fixed” capacity costs over a larger volume of products. Although this could be true, it is not necessarily true. Even without any fixed costs or finite capacity, the firm could earn higher profits by selling more items or by shifting its mix from low-to high-margin products. The sales activity variance in panel A of Table 2 arises from the changes in contributions from selling different (from budgeted) quantities of output while holding fixed costs, selling price, and unite-variable costs constant, not from absorbing more or fewer fixed expense into product costs.

Second, we object to treating the sales activity (or the capacity usage) variance as a component of the overall productivity measure. We believe productivity improvement arises from better process for using materials, labor, energy, support departments, and capital to produce a given quantity of output. We think it misleading to attribute productivity gains merely to increased activity levels.

Usage variances are computed in panel B of table 2. The difference between standard and actual consumption of input resources at the actual production volume for the year is evaluated using the standard price for each input resource. The productivity variance, defined as the sum of the individual usage variance, is favorable $12,195, with the favorable usage variance labor and materials outweighing the unfavorable usage variances for variable and discretionary overhead. The productivity variance represent the increase in profits from better usage of individual inputs

to produce a given quantity of outputs. The actual volume and mix of outputs, prices of output products, and prices of input resources are accounted for in other variance so that the productivity variance is influenced neither by production activity nor by relative price effects.

Note that the productivity variance is not simply an aggregation of the typically computed usage variances for direct or variable costs. The productivity variance includes a component for efficient use of discretionary fixed costs. Since discretionary fixed costs constitute a significant percentage of total costs in today’s complex manufacturing environments, productivity improvements must arise not only from controlling variable costs, but also from controlling the quantity of discretionary fixed overhead costs into quantity and price components as they do when disaggregating material and labor variances. We can achieve this separation into quantity and price effects for overhead because we identify the transactions or cost drivers that cause the quantity of discretionary fixed overhead resources to vary.

In the third and final calculation, price variance are computed separately for outputs and outputs and then added together to obtain a price recovery variance (see panel C and table 2). The favorable $30,000 sales price variance for Product A exceeds the unfavorable price variance for material ($16,400), variable overhead ($4,976), discretionary overhead ($591), and committed overhead ($2000) to produce a favorable price recovery variance of $6,033. This favorable variance indicates that, at actual outputs and inputs, the increased prices of output products generated revenues in excess of the higher costs from increased prices for input resources.

The difference between actual profits of $126,368 in year 2 and the expected or budgeted profits for year 2 of $110,140 can now be explained as:

Profit variance= sales activity variance + productivity variance + price recovery variance.

The accounting variances computed in the previous section help separate a total profit variance into components caused by increases in the volume of sales, changes in relative prices between outputs and inputs, and productivity effects. But the variances components are themselves absolute numbers and do not translate readily into percentage improvements. Particularly when measuring productivity changes, managers generally find it easier to evaluate productivity improvements by percentages, such as by specifying a target for overall productivity improvement of, say, 7 percent annually. The percentage comparison facilities comparison both

to scale of operations and to previous years. A favorable productivity variance of $200,000 is more impressive when total operating cost are $2 million than when they are $200 million. And a company that’s sets a goal of improving productivity by 7 percent each year will not find it immediately informative to learn that its productivity variance this year was a favorable $200,000.

In this section, we demonstrate how to represent the arithmetic productivity, price recovery, and sales activity variances of the preceding section as percentage or ration measurement. These ratio measures also permit us to compute elasticity number that indicate the sensitivity of overall profits to percentage changes of each of to percentage changes in each of the three operating factors.

In order to obtain percentage measures for the three aggregate variance measures, we must choose an appropriate denominator to normalize each measure. Standard costs or margins represents plausible bases for computing the percentage changes in productivity, price recovery, and sales activity.

Table 3 presents the calculation of the ratio s to explain the 14.73% increase in profits (actual profits=$126,368; budgeted profits=$110,140). First the unfavorable-$2,000 sales activity variance represents contribution margin of $210,000. The $12,195 favorable productivity variance is a 1.63 percent reduction in actual costs relative to the standard costs of $747,860, computed at the actual volume and mix of out puts (6,000 units of product 1, 20,000 units of product2) finally the favorable $6,033 price recovery variance with respect to the standard margin of $120,335, computed with standard prices and costs at the actual quantities of outputs sold and inputs consumed.

In order to combine the three ratios, each normalized by a different activity base, into an overall measure of profit improvement, we compute weights (see column (2) of Table 3) as the ratio of each denominator measure (used to compute column (0 in the table) to the budgeted profit of $110,140. Multiplying each weight in column (2) by the respective percentage change in column (1) yield the weighted percentage changes in column (3) produces the overall percentage improvement in profits of 14.73 percent. We can now explain the 14.73 percent increase in profits by productivity increase contribution 11.07 %, a favorable price recovery experience of 5.47 percent, less a 1.82 percent drop in sales activity.

In addition to this ability to decompose a percentage change in profits into sales activity, productivity , and price recovery components, we also obtain parameters of some economic significance. The weights we computed for column (2) of table 3 can interpreted as elasticity of profits with respect to changes in productivity, price recovery, and sales activity. For example the productivity weight of 6.79 implies that every 1 percent increase in productivity produces a 6.79% increase in total profits (holding prices and output quantities constant). The price recovery weight of 1.09 implies that a 1 percent improvement in gross margin due to changing output and input prices will produce a 1.09% increase in profit (holding productivity, prices and costs constant). Thus the weighs serve to decompose a traditional accounting variance a into a ratio plus an elasticity factor that determines the sensitivity at profits to percentage changes in operating and activity factors.

Now that the relationship between the variances standard cost accounting system and productivity ratio measures has been developed, we can analyze a commonly cited productivity measurement system proposed by the American Productivity Center (APC), which is based only on the actual quantities and prices outputs and inputs over a period of time. We briefly describe the APC system using the data in Table 4.

The goal of explaining changes in actual profits from one period to the next sounds straightforward but actually differs from both the goal traditional productivity measurement systems and that of standard cost accounting systems. The APC measurement system only explains changes in profits not caused by changes in output levels. Recall from section 5 that the APC system signaled a 3.08% profitability change between two periods when actual profits increased 34 percent. Standard cost variance analysis, as a one period-to-period (as accomplished by SC) but does not explain the period-to period changes in profitability. It is straightforward, by combining the best features from both approaches, to evaluate profit performance over time.

Our proposed system for reconciling actual performance in successive periods can be easily expressed by the diagram in Figure 1. The productivity, price recovery, and sales activity variances shown in the figure are analogous to those computed in sections 3 and 4. The major innovation occurs in computing a usage standards variance to incorporate expected changes in the manufacturing process between years 1 and 2. This variance will control for any changes introduced in the standards for input quantities per unit of output. As before, we illustrate the analysis via a numerical example.

Table 5 contains the standards for two years of operating and the revenue, cost, quantity, and price information for two years. The two productivity variances (for Years 1 and 2) and the price

recovery variance between years 1 and 2 are computed exactly as illustrated in section 3 (see Table 4, panels B and C) with the exactly price in year 1 being used instead of the budgeted price in year 2. Just for illustrated, we show the computation of the productivity performance in year 1 (actual consumption is below standard) is shown with a negative sign to signify that good productivity performance in year is lower when compared to actual performance in Year 1 than when compared to budgeted or standard performance in year 2 is lower when the performance in year 1 than when compared to budgeted or standard performance in year 1.

Panel B of Table 6 shows the calculation of the sales activity variance, which differs slightly from that shown in Section 3 (in panel A of Table 2). In the multi-period calculation, margins are computed using the actual prices and the standard usage quantities from year 1. In the previous section. The contribution margin for computing the sales variance was a function of standard prices and standard usage quantities in year 2. Otherwise the calculations are identical.

The main innovation is the calculation of a usage variance to reflect changes in the input consumption standards between year and year 2. The tighter standards in year 2 reflect productivity improvement made during year 1 that need to be in corporate when computing actual productivity gains from year 1 to 2. The calculation proceeds by evaluating, at the actual output levels of year2 the calculation proceeds by evaluating, at the actual output level. The difference in the standard consumption of outputs between year 1 and year 2. For example year 2 output is 60,000 units of product A and 20,000 units of product B. using year 1 material quantity standards, the standard material consumption of outputs between year 1 and year 2 is given below. For example year 2 output is 60,000 units of product A and 20,000 units of product B. using year 1 material quantity standards, the standard material consumption is 60,000* 1 + 20,000*1,24= 84,800 units of materials. Based on the tighter year 2 standards the standard material consumption is 60,000 * 1 + 20,000 1.20 = 84,000 units

The difference in each input quantity is priced out using the actual input prices in year 1, the difference of 800 units of material (84800-84000) is multiplied by the $5 actual price in year 1 to produce the $4,000 materials usage standards variance of $20,740. Note that no usage standards variance is computed for committed fixed overhead since the quantity of such overhead is assumed not o be reducible (at least within the time frame of the productivity analysis). Also the usage standards variance we have define includes a component for a standards change in discretionary fixed costs. This recognizes the importance of achieving productivity gains in discretionary overhead by reducing the number of transactions and the unit costs of these transactions required to produce a given amount of output. Traditional accounting variance

analysis and productivity measurements have ignored the opportunities for productivity gains in the of discretionary overhead.

The overall change of $9,383 in actual profits from year 1 to year 2 can now be decomposed into an aggregate productivity variance ( equal to the productivity variance sum of the year1 and year 2, and the usage standards variance between years 1 and years 20 plus price recovery and sales activity variances.

As in section 4 , we can combined the accounting variances computed in Table 6 into ratio measures suitable for measuring evaluating performance between the two years.

Each of the five accounting variances computed in Table 6 is normalized by an appropriate denominator:

The computations for each accounting variance are shown In the last column of Table 6 panel A-C, and in Table 2, panels B and C

In order to combine the various ratios, each normalized by a different activity base, into an overall measure of profit, we compute the weights shown in column (20) of Table 8. The weights are the ratio of each denominator measure to the profits in year 1 of $116.985. Multiplying each weights in column (2) by the respective percentage changes in column (3) of the Table.

The actual change in profits between years 1 and 2 can now be explained in terms of the performance ratios in column (3) of table 8. The actual change in profit between year 1 and 2 equals $126,368-$116,985=$9,383 or 8.02% percent of year 1’s profits . that portion of this increase due to productivity improvements can be computed as the sum.

The 10.42 percent improvement in productivity during year 2 represents the actual performance in year 2 relative to the standards for that year. The 17.73 percent improvement in usage in standards represents the tighter standards in year 2 relative to year 1. Therefore, relative to the standards in existence during year1, the productivity performance during year 2 is 28.15% higher. But the -24.78 % figure for productivity during year 1 indicates that year 1’s actual

performance was already much better than its standards for that year so that the actual improvement in productivity between years 1 and 2 nets out to 3.37%

The extent of profit change due to price recovery improvement is 5.16 percent. This indicates that prices changed favorably, particularly for product 1, without corresponding increases in the prices of resource inputs. The increase prices for variable overhead and materials were more than offset by an increase in the price of product 1 to yield a 5.16 %

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Our synthesis of productivity measurement and standard cost systems has yielded useful insights. First, the intra period standard cost variance analysis, comparing actual costs between successive periods, to provide a more systematic and comprehensive explanation of changes in profitability each period and over time. Overall changes in productivity can be decomposed into three components: changes in sales activity (including both volume and mix effects), changes in productivity, and changes in price recovery. By explicitly recognizing the profit change component caused by changes in the mix and volume of outputs, we do not permit confounding effects of increases or decreases in sales to distort the measurement of either productivity or price recovery changes, a distortion that exists in traditional productivity measurement systems.

Strategic management accounting practices exist in different forms within companies seeking to use both financial and non financial information as well as external market-based information. It is also subject to wider contextual influences including industry-specific effects. Strategic management accounting systems and strategic variance analysis are common and well-utilized. Their implementation and effects on companies are best considered in visionary and creative terms. Apart from cost and benefit analysis, understanding organizational context from a long term spectrum is the key to implementation of an effective strategic management accounting system.